Bayesian Portfolio Optimization by Predictive Synthesis
This work addresses the challenge for investors in accurately estimating asset return distributions, offering a meta-learning approach that is incremental over existing ensemble methods.
The paper tackles the problem of portfolio optimization under uncertain financial market conditions by proposing a Bayesian predictive synthesis method to combine multiple asset return prediction models, resulting in improved portfolio performance as demonstrated through mean-variance and quantile-based portfolios.
Portfolio optimization is a critical task in investment. Most existing portfolio optimization methods require information on the distribution of returns of the assets that make up the portfolio. However, such distribution information is usually unknown to investors. Various methods have been proposed to estimate distribution information, but their accuracy greatly depends on the uncertainty of the financial markets. Due to this uncertainty, a model that could well predict the distribution information at one point in time may perform less accurately compared to another model at a different time. To solve this problem, we investigate a method for portfolio optimization based on Bayesian predictive synthesis (BPS), one of the Bayesian ensemble methods for meta-learning. We assume that investors have access to multiple asset return prediction models. By using BPS with dynamic linear models to combine these predictions, we can obtain a Bayesian predictive posterior about the mean rewards of assets that accommodate the uncertainty of the financial markets. In this study, we examine how to construct mean-variance portfolios and quantile-based portfolios based on the predicted distribution information.